Developing and deploying a use-inspired metapopulation framework for stratified health outcomes

People involved

Roadmap

• Motivation & collaboration context
  
• MetaRVM modeling framework
  • Metapopulation structure
    
  • Disease progression model
    
  • Mixing matrices from a synthetic population
    
• Implementation as an R package
• Trajectory-oriented optimization
• Case study: influenza in Chicago

Motivation

• Public health decision makers increasingly rely on epidemiological models to:
  • Forecast likely futures
  • Stress-test interventions
  • Plan resource allocation (e.g., beds, staffing)

Motivation

Motivation

MetaRVM - Disease model

MetaRVM - Population mixing

• Subpopulations based on age: children (green boxes), working-age adults (blue boxes), and seniors (orange boxes).
• Different mixing patterns for daytime and nighttime.

Mixing and force of infection

• For each stratum, the force of infection depends on:
  • Mixing rates between strata \(M = (m_{ij})\)
  • Prevalence of infectious individuals in \(j\)th stratum \(I_t^{(j)}\)

Mixing and force of infection

• Effective interacting population (exclude H, D)

\[ \mathrm{MP}_t^{(j)} = P_t^{(j)} - H_t^{(j)} - D_t^{(j)}, \qquad j \in \mathcal{J}. \]

• Mixing across demographic strata

\(M\): mixing matrix \[ M = (m_{ij}), \qquad \sum_{j \in \mathcal{J}} m_{ij} = 1 \ \forall i, \]

\[ \mathrm{MP}_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,\mathrm{MP}_t^{(i)}, \quad S_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,S_t^{(i)}, \quad I_{t,\text{eff}}^{(j)} = \sum_{i \in \mathcal{J}} m_{ij}\,I_t^{(i)}. \]

Mixing and force of infection

• Force of infection (susceptible vs vaccinated)

\[ \lambda_{s,t}^{(j)} = \beta_s \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}}, \qquad \lambda_{v,t}^{(j)} = \beta_v \frac{I_{t,\text{eff}}^{(j)}}{\mathrm{MP}_{t,\text{eff}}^{(j)}}. \]

• S → E transition in stratum (j)

\[ p_{\mathrm{SE}}^{(j)} = 1 - \exp\!\big(-\lambda_{s,t}^{(j)} \,\Delta t\big), \\ \Delta SE_t^{(j)} = p_{\mathrm{SE}}^{(j)}\,S_t^{(j)}. \]

Age-stratified model - illustration

What we need

• Population counts
  • Source: Census
• Daily vaccination schedule
  • Source: CDPH
• Mixing matrices
  • Source: ChiSIM synthetic population

Age-stratified model - illustration

Synthetic population

• Statistically representative synthetic population of Chicago
  (2.7M people, 1.4M places, 13k activity schedules)

• Demographically accurate households from ACS + PUMS at the CBG level

• Workplaces generated from County Business Patterns + LEHD OD data
  (individuals assigned to realistic work locations)

• Schools assigned to all school-aged children
  (some adults assigned schools as workplaces)

• Additional mixing locations (restaurants, gyms, etc.) from SafeGraph

Age-stratified model - illustration

Mobility –> Mixing

• Agent mobility from real schedules

  • Adults: American Time Use Survey
  • Children: Panel Study of Income Dynamics
  • Each agent draws 1 of 10 possible schedules daily (weekday/weekend)

• Endogenous contact network emerges from hour-by-hour co-location of agents

• Aggregate

  • Count the total number of contacts that occur at each location between and within subpopulations
  • Normalize the counts to arrive at per-capita value

Daytime vs Nightime mixing

Vaccination

TOO: overview

• Calibration goal:
  • Align model-generated hospitalizations with observed time series
• Approach:
  • Choose a set of calibration parameters (e.g., transmission scaling, reporting/observation fraction)
  • Define a loss function (e.g., squared error between modeled and observed hospitalizations)
  • Use Bayesian optimization to:
    • Explore parameter space efficiently
    • Balance exploitation and exploration
• Outcome:
  • Posterior-like distribution over plausible parameter values
  • Uncertainty envelopes around model trajectories

Results

Summary

• MetaRVM provides a use-inspired metapopulation modeling framework for:
  • Detailed, stratified tracking of health outcomes
  • Infectious disease spread across multiple interacting subpopulations
• Key contributions:
  • Uses synthetic population-derived mixing matrices
  • Balances realism with computational efficiency
  • Delivered as an open-source R package with a Shiny front end
• Case study:
  • Demonstrated on influenza-related hospitalizations in Chicago
  • Supports prospective surveillance and scenario analysis

Funding acknowledgments

• Chicago Department of Public Health

Questions?

Thank you!